Non-linear relationship between pulse pressure and the risk of prediabetes: a 5-year cohort study in Chinese adults

Previous research has established a strong link between pulse pressure (PP) and diabetes, but there is limited investigation into the connection between PP and prediabetes. This study aims to explore the potential association between PP and prediabetes. A retrospective cohort study encompassed 202,320 Chinese adults who underwent health check-ups between 2010 and 2016. Prediabetes was defined in accordance with the World Health Organization criteria, indicating impaired fasting glucose, with fasting blood glucose levels ranging from 6.1 to 6.9 mmol/L. To assess the PP-prediabetes relationship, we employed Cox regression analysis, sensitivity analysis, and subgroup analysis. Cox proportional hazards regression, coupled with cubic spline functions and smooth curve fitting, helped elucidate the non-linear PP-prediabetes relationship. Upon adjusting for confounding factors, we observed a positive association between PP and prediabetes (HR 1.15, 95% CI 1.11–1.18, P < 0.0001). Participants in the fourth quartile (PP ≥ 51 mmHg) had a 73% higher likelihood of developing prediabetes compared to those in the first quartile (PP < 36 mmHg) (HR 1.73, 95% CI 1.52–1.97, P < 0.0001). Moreover, the relationship between PP and prediabetes was non-linear. A two-piece Cox proportional hazards regression model identified an inflection point at 40 mmHg for PP (P for log-likelihood ratio test = 0.047). Sensitivity and subgroup analyses corroborated the robustness of our findings. Our study reveals a non-linear correlation between PP and prediabetes, signifying an increased risk of prediabetes when PP levels exceed 40 mmHg. This discovery has significant clinical implications for early prediabetes prevention and intervention, ultimately contributing to improved patient outcomes and quality of life.

www.nature.com/scientificreports/ to hypertension causing insulin resistance and affecting insulin secretion and insulin receptor sensitivity 8,9 .Traditionally, studies have primarily utilized systolic and diastolic blood pressure to assess cardiovascular risk.However, pulse pressure (PP), defined as the difference between systolic and diastolic blood pressure, is also a valuable indicator of blood pressure and is frequently used to measure arterial elasticity and assess the cardiovascular system's functional status 10 .A low PP may indicate reduced arterial elasticity or impaired cardiac pumping function, resulting in inadequate organ and tissue perfusion 11 .Prolonged elevated PP may increase the strain on the heart and blood vessels, thereby increasing the risk of cardiovascular disease 11,12 .Some studies have found higher PP may be associated with an increased risk of cardiovascular diseases such as arteriosclerosis, coronary heart disease, and stroke [13][14][15][16] .Several studies have found that higher PP is associated with an increased risk of diabetes 17,18 .This suggests that PP could serve as a novel marker for predicting the onset of diabetes.
However, research on the relationship between PP and prediabetes is currently limited.A study from 2018 investigated the connection between PP and insulin resistance in individuals with prediabetes.The findings revealed a positive correlation between PP and insulin resistance, suggesting that an increase in PP could independently predict insulin resistance 8 .Nevertheless, these observations are based on observational studies and cannot establish a causal relationship between PP and prediabetes.Consequently, further research is necessary to validate and gain a better understanding of the association between PP and prediabetes.
To delve deeper into the link between PP and prediabetes, we have designed a large-scale cohort study involving 202,320 participants from 32 locations in 11 cities across China.Our goal is to analyze the potential association between PP and the future risk of prediabetes.By doing so, we aim to demonstrate PP as a promising marker for predicting prediabetes, offering valuable insights for prediabetes screening and diabetes prevention strategies.

Baseline characteristics of participants
Table 1 shows the demographic and clinical characteristics of the study participants.In the present study, 202,320 individuals were included.The mean age was 41.57 ± 12.36 years old.109,410 (54.08%) individuals were men, and 92,910 (45.92%) individuals were women.A total of 202,320 individuals developed prediabetes after a follow-up period of an average of 3.12 years.PP divided into four groups based on quartiles: Q1 ≤ 36 mmHg; 37 < Q2 ≤ 42 mmHg; 43 < Q3 ≤ 50 mmHg; Q4 > 51 mmHg.As shown in Table 1, compared to Q1, the Q4 group had higher levels of age, systolic blood pressure (SBP), diastolic blood pressure (DBP), BMI, AST, ALT, TG, LDL-C, TC, BUN, Scr, FPG1 and FPG2.Additionally, the Q4 group had a higher proportion of males, smokers, drinkers, and individuals with a family history of diabetes.In comparison to Q4, the Q1 group had higher levels of HDL-C.
As depicted in Fig. 2, the Kaplan-Meier curves demonstrate the survival probability of not progressing to prediabetes.There is a significant variation in the risk of developing prediabetes among the four PP groups (P < 0.0001).With an increase in PP levels, the probability of not developing prediabetes gradually declines.This indicates that the group with the highest PP exhibits the highest risk of progressing to prediabetes.

Univariate analysis
As shown in Table 3, age, BMI, TC, TG, AST, ALT, LDL-C, BUN, Scr and PP were positively associated with the risk of prediabetes.Conversely, HDL-C showed a negative association with the risk of prediabetes.Furthermore, women exhibited a lower risk of developing prediabetes compared to men.Additionally, individuals who abstained from alcohol and tobacco had a reduced risk of developing prediabetes.

The relationship between PP and prediabetes
As shown in Table 4, in the unadjusted model, the HR (95% CI) for the association between PP and prediabetes was 1.42 (1.39, 1.44).In the minimally-adjusted model, after adjusting for gender and age, the HR (95% CI) was 1.19 (1.16, 1.21).In the fully-adjusted model, after further adjusting for gender, age, BMI, TG, LDL-C, HDL-C, AST, ALT, BUN, Scr, family history of diabetes, drinking status, and smoking status, the HR (95% CI) was 1.15 (1.11, 1.18).This indicates that for every 10-mmHg increase in PP, the risk of prediabetes increases by 15%.Additionally, when we categorized PP into four groups, in the fully-adjusted model, the risk of developing prediabetes in Q4 was 1.64 times higher than in Q1 (HR (95% CI) 1.64 (1.44, 1.87).

The results of sensitivity analysis
We conducted a series of sensitivity analyses to ensure the reliability of our research findings (Tables 4 and 5).Firstly, we used a Generalised Additive Model (GAM) to incorporate a continuous covariate as a curve into the equation.The results from Model III, as shown in Table 4, were consistent with those from the fully adjusted model (HR 95% CI 1.19 (1.15-1.23),P < 0.001).Additionally, we performed sensitivity analyses on participants with a BMI below 28.After adjusting for potential confounding variables (including age, sex, BMI, HDL-c, TG, LDL-c, BUN, Scr, ALT, AST, family history of diabetes, smoking and drinking status), the results indicated a

P for trend ＜0.01
The rate of prediabetes (%) The incidence rate of prediabetes.The incidence rate of prediabetes was significantly higher in participants from Q4 compared to those in the lower PP groups (P < 0.01 for trend).
Vol:.( 1234567890) positive association between PP and the risk of prediabetes (HR 95% CI 1.19 (1.15-1.23),P < 0.001).We also excluded participants aged 60 years or older for further sensitivity analysis.After adjusting for confounding variables, the results still showed a positive correlation between PP and the incidence of prediabetes (HR 95% CI 1.28(1.23-1.34),P < 0.001).Moreover, when excluding participants without a family history of diabetes and adjusting for relevant variables, the results demonstrated a positive association between PP and the risk of prediabetes (HR 95% CI 1.15 (1.11-1.18),P < 0.001) (Table 5).

The non-linear relationship between PP and prediabetes
We utilized a Cox proportional hazards regression model with cubic spline functions and found a non-linear correlation between PP and the probability of developing prediabetes (Fig. 3).To better fit the data, we employed a standard binary two-piecewise Cox proportional hazards regression model and selected the best model using the log-likelihood ratio test (Table 6).The P value for the log-likelihood ratio test was less than 0.05.Using a recursive technique, we identified 40 mmHg as the inflection point for PP.After the inflection point, the hazard ratio (HR) for PP and the risk of developing prediabetes was 1.17 (95% CI 1.13, 1.22, P < 0.0001).However, before the inflection point, the HR for PP and the risk of developing prediabetes was 1.01 (95% CI 0.89, 1.15, P = 0.8916), which was not statistically significant.

Subgroup analysis
We conducted subgroup analysis to investigate potential additional risk factors that could influence the relationship between PP and prediabetes risk.We examined the impact of BMI, age, gender, smoking status, drinking status, and family history of diabetes as stratification factors.However, our analysis revealed that drinking status, smoking status, family history of diabetes had no significant impact on the association between PP and prediabetes risk.Additionally, there was a stronger connection between PP and risk of prediabetes in individual with age < 60 years, BMI < 24, and females (Table 7).

Discussion
After conducting a comprehensive analysis, we discovered a non-linear relationship between PP and the risk of prediabetes.Furthermore, we pinpointed a critical threshold of 40 mmHg for PP.When PP exceeded 40 mmHg, a significant positive association with prediabetes risk was observed (HR: 1.17, 95% CI 1.13-1.22,P < 0.0001).However, when PP was below 40 mmHg, this association did not reach significance (HR: 1.01, 95% CI 1.08-1.15,P = 0.8916).Notably, a stronger connection between PP and prediabetes risk was evident in individuals under the age of 60, those with a BMI under 24, and females.These findings offer valuable insights into the relationship between PP and prediabetes risk, emphasizing the importance of monitoring PP levels when assessing prediabetes risk.Further research is warranted to uncover the underlying mechanisms and explore potential interventions tailored to individuals at risk of prediabetes based on their PP levels.
In recent years, researchers have conducted extensive studies on the relationship between PP and diabetes.Some studies have found that high PP is associated with an increased risk of diabetes.Several large-scale studies www.nature.com/scientificreports/conducted in China found that an increase in PP is associated with an increased risk of type 2 diabetes 17,18 .Insulin resistance is one of the main pathophysiological mechanisms of diabetes.A study in Korea confirmed a positive correlation between PP and insulin resistance in non-diabetic adults 8 , suggesting that high PP may be associated with the development of insulin resistance.
Prediabetes is closely related to the development of diabetes, as it is considered a pre-diabetic state 19 .However, research on the relationship between PP and prediabetes is scarce.Going back to 1992, Cederholm et al. found a correlation between higher PP and impaired glucose tolerance (IGT) in a study 20 .However, this study only included 695 middle-aged subjects and only analyzed the higher PP group's increased likelihood of developing prediabetes.The association between PP and prediabetes was not thoroughly examined.Roengrit et al. observed significantly higher PP in the impaired fasting glucose (IFG) group compared to the normal fasting glucose Table 5. Relationship between PP and the risk of prediabetes in different sensitivity analyses.Crude model I was a sensitivity analysis performed after excluding participants with BMI ≥ 28 mmol/L (N = 15,967).we adjusted age, sex, ALT, AST, BUN, Scr, TG, LDL-c, HDL-c, family history of diabetes, drinking status, and smoking status.Model II was a sensitivity analysis performed after excluding participants with age ≥ 60 mmol/L (N = 21,599).we adjusted sex, BMI, ALT, AST, BUN, Scr, TG, LDL-c, HDL-c, family history of diabetes, drinking status, and smoking status.Model III was a sensitivity analysis performed on participants without family of diabetes.We adjusted age, sex, BMI, ALT, AST, BUN, Scr, TG, LDL-c, HDL-c, smoking status and drinking status.HR, Hazard ratios; CI, confidence, Ref, reference.www.nature.com/scientificreports/(NFG) group (P < 0.05) and confirmed a positive correlation between PP and fasting blood glucose (r = 0.20, P = 0.01) 21 .However, this study was a small case-control study and cannot establish a causal relationship between PP and prediabetes.To date, there is no research analyzing the causal relationship between PP and prediabetes.
Based on these circumstances, we hypothesized that there might be a positive correlation between PP and the risk of developing prediabetes.To test this hypothesis, we included 202,320 participants without diabetes from 32 regions in 11 cities in China and followed them for 5 years to analyze the relationship between PP and the risk of developing prediabetes through multivariable Cox regression analysis.Our study showed a non-linear relationship between PP and prediabetes and we calculated the inflection point of PP to be 40 mmHg.When PP levels were below 40 mmHg, there was no association with the occurrence of prediabetes (HR: 1.01, 95% CI 0.89-1.15,P = 0.8916).However, when PP levels were above 40 mmHg, there was a 17% increased risk of developing prediabetes for every 10 mmHg increase (HR: 1.17, 95% CI 1.13-1.22,P < 0.0001).This suggests that we can predict the risk of developing prediabetes based on PP values.Compared to other studies, our research delves more profoundly into the association between PP and prediabetes risk.Firstly, prior research primarily relied on cross-sectional designs, whereas our study employs a cohort study design, enhancing our understanding of the PP-prediabetes relationship among Chinese adults.Secondly, we conducted a comprehensive examination of this relationship using multivariable Cox regression analysis, accounting for variables like BMI, Scr, smoking, alcohol use, and family history of diabetes, all of which are associated with prediabetes risk 22,23 .
Our study utilized a Cox proportional hazards regression model along with cubic spline functions and smooth curve fitting to uncover the non-linear PP-prediabetes relationship.We bolstered the reliability of our findings through a series of sensitivity and subgroup analyses, affirming the stability of the PP-prediabetes relationship.Notably, we identified a stronger positive correlation in females and individuals under the age of 60, with a BMI under 24 kg/m 2 .Significantly, our study calculated the inflection point for PP, offering valuable clinical guidance for mitigating prediabetes risk.
Our study's clinical implications are noteworthy.Firstly, individual PP values can serve as predictive indicators for prediabetes risk, enabling healthcare professionals and patients to anticipate and implement timely interventions, such as lifestyle adjustments, increased physical activity, and dietary control.Secondly, our research establishes reference values for target PP levels.When PP surpasses 40 mmHg, healthcare professionals can recommend proactive measures to mitigate prediabetes risk, including regular blood pressure monitoring, medication adjustments, and lifestyle improvements.Lastly, our findings present a novel perspective on the PP-prediabetes relationship, moving beyond linear associations commonly studied.This non-linear relationship discovery opens new avenues for exploring the mechanisms connecting PP and prediabetes.In summary, our study highlights a non-linear connection between PP and prediabetes, emphasizing increased risk when PP exceeds 40 mmHg.This discovery holds clinical significance for early prevention and intervention in prediabetes, enhancing patient outcomes and quality of life.The mechanism by which high PP increases the risk of prediabetes is not fully understood and may be related to several factors.Firstly, high blood pressure can lead to insulin resistance, which is a reduced response of the body to insulin 24 .And high PP may be associated with increased levels of inflammation and oxidative stress 25,26 .Inflammation and oxidative stress are important mechanisms in the development of prediabetes.Finally, high PP may be related to dysregulation of the neuroendocrine system.
However, it is important to acknowledge the potential limitations of this study.Firstly, as a retrospective analysis of a cohort study, there may be unaccounted factors that could influence the relationship between PP and prediabetes, such as dietary habits and physical activity levels, despite adjusting for various factors.Secondly, the diagnosis of prediabetes in this study was primarily based on impaired fasting glucose, which may underestimate the true incidence of prediabetes compared to using additional diagnostic criteria like oral glucose tolerance test (OGTT) or glycated hemoglobin (HbA1c).Moreover, the average follow-up period of 3.12 years may not capture the long-term relationship between PP and prediabetes, and a longer follow-up duration would provide more robust results.Additionally, the data used in this study were derived from specific regions in China, which may limit the generalizability of the findings to the entire Chinese population.Future research should aim to include larger and more diverse populations to enhance the external validity of the results.Finally, apart from PP, our study has yet to incorporate more composite indicators such as hypertriglyceridemic waist-to-height ratio, TyG index, non-HDL cholesterol, residual cholesterol, among others.To enhance our understanding of diabetes, future research could further analyze the relationship between these composite indicators and both diabetes and prediabetes, thereby comprehensively advancing the prevention and treatment of diabetes.Thank you for your attention and support.

Conclusion
We have made a novel discovery a non-linear relationship between PP and prediabetes risk.Specifically, we found that the risk of prediabetes significantly increases when PP ≥ 40 mmHg, while there is no significant change in prediabetes risk when PP < 40 mmHg.This finding emphasizes the importance of considering PP as a potential risk factor for prediabetes.

Data source
We retrieved raw data from the Dryad Digital Repository, a publicly accessible data repository.The dataset used in our study can be accessed at Dryad data repository (dataset: https:// datad ryad.org/ stash/ datas et/ doi: 10. 5061% 2Fdry ad.ft875 0v) and conducted a secondary analysis of a medical examination program using publicly available data provided by Chen et al. 27 .

Study population
The original dataset was derived from the Rich Healthcare Group's computerized database in China, encompassing medical records from health check-ups conducted between 2010 and 2016 across 32 regions and 11 cities.Out of the initially enrolled 685,277 Chinese adults over 20 years old, each having at least two visits.Participants with follow-up fasting plasma glucose levels between 6.1 and 6.9 mmol/L and no new reports of diabetes were included in the study.Exclusions were made for individuals with a diabetes diagnosis at both baseline and followup, unclear diabetes status at follow-up, extreme BMI (outside the range of 15-55 kg/m 2 ), missing baseline data for weight, height, sex, DBP, SBP, or fasting plasma glucose (FPG), or those with FPG levels over 5.6 mmol/L at baseline and exceeding 6.9 mmol/L during follow-up, including any new diabetes diagnoses.This resulted in a final cohort of 202,320 participants (Fig. 4).

Figure 3 .
Figure 3.The non-linear relationship between PP and risk of predibetas.A non-linear relationship between them was detected after adjusting for gender, age, BMI, TG, HDL-c, LDL-C, AST, ALT, Scr, BUN, family history of diabetes, drinking status, smoking status.

6852277
Chinese participants ≥20 years old with at least two visits in 2010-2016 473444 were excluded 1) 103946 had available weight and height measurements 2) 1 had no available information on gender 3) 152 had extreme BMI values (<15kg/m2 or >55 kg/m2) 4) 31370 had no available FPG value at baseline 5) 324233 had visit intervals less than 2 years 6) 7112 diagnosed with diabetes atbaseline 7) 6630 undefined diabetes status at follow-up According to the data source article: According to this studying: 202320 Chinese participants were included in the study 1) 4174 diagnosed with diabetes during follow-up 2) 5257 had FPG≥6.1mmol/l at baseline 3) 61 had FPG 6.9mmol/l during follow-up 4) 21 had no available DBP or SBP value

Table 1 .
The baseline characteristics of participants.Continuous variables were summarized as mean (SD) or medians (quartile interval); categorical variables were displayed as percentage (%).BMI body mass index, SBP

Table 3 .
Risk of prediabetes analyzed by univariate Cox proportional hazards regression.

Table 6 .
The result of the two-piecewise Cox proportional hazards regression model. Outcome:

Table 7 .
Stratified associations between PP and risk of prediabetes by age, sex, smoking status, and drinking status.